🏢 Max Planck Institute for Intelligent Systems

This paper introduces Causally Fair DRO, a novel framework for robust optimization that addresses individual fairness concerns by incorporating causal structures and sensitive attributes, providing th…

LLM survey responses are systematically biased, often masking genuine model capabilities and leading to misleading alignment conclusions.

Learning hidden structures from sparse interactions in data is computationally hard but can be achieved with sufficient samples using gradient-based methods; This is shown by analyzing the gradient dy…

New research quantifies how search engines steer web traffic by subtly changing results, offering a powerful method for antitrust investigations and digital market analysis.

A novel post-processing framework, based on a d-dimensional generalization of the Neyman-Pearson lemma, optimally solves multi-objective learn-to-defer problems under various constraints, improving co…